Analog Gated Recurrent Unit Neural Network for Detecting Chewing Events

IEEE Trans Biomed Circuits Syst. 2022 Dec;16(6):1106-1115. doi: 10.1109/TBCAS.2022.3218889. Epub 2023 Feb 14.


We present a novel gated recurrent neural network to detect when a person is chewing on food. We implemented the neural network as a custom analog integrated circuit in a 0.18 μm CMOS technology. The neural network was trained on 6.4 hours of data collected from a contact microphone that was mounted on volunteers' mastoid bones. When tested on 1.6 hours of previously-unseen data, the analog neural network identified chewing events at a 24-second time resolution. It achieved a recall of 91% and an F1-score of 94% while consuming 1.1 μW of power. A system for detecting whole eating episodes-like meals and snacks-that is based on the novel analog neural network consumes an estimated 18.8 μW of power.

Publication types

  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Humans
  • Mastication*
  • Neural Networks, Computer*